Neural Network and Genetic Algorithm Based Dynamic Obstacle Avoidance and Path Planning for A Robot

Abstract

A method of dynamic obstacle avoidance and path planning based on neural network and genetic algorithm is proposed. The neural network model of dynamic environmental information in the workspace for a robot is constructed. Using this model, the relationship between dynamic obstacle avoidance and the output of the model is established. Then the two dimensional coding for the via points of the path is converted to one dimensional one and the fitness of both dynamic obstacle avoidance and the shortest distance are fused to a fitness function. The simulation result shows that the proposed method is correct and efficient.